Recurrence Capture of Liver Disease Using Support Vector Machine with Improved Particle Swarm Optimization
نویسندگان
چکیده
The ability to discover patient acuity or severity of illness has immediate practical use for clinicians. Evaluate the use of multivariate time series modeling along with multiple models. To evaluate the data-merging algorithm, performance of prediction using processed multiple measurements are compared to prediction using single measurements. This scenario is used to perform the multiple time series data processing along with multiple measurements. The merging algorithm also statistical measures are performed and calculated based on the specified dataset. However it as issue with unbalanced data and classification performance is reduced significantly. To avoid this issue in proposed scenario, the proposed algorithm named as improved Particle swarm Optimization algorithm (IPSO) is used for feature selection. This algorithm is used to increase the prediction accuracy. The experimental result concludes that proposed system provides greater performance rather than existing scenario. SVM classifier is used to classify whether the disease is
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